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Oct 28, 2006

Brain-machine interfaces: past, present and future

Brain-machine interfaces: past, present and future.

Trends Neurosci. 2006 Sep;29(9):536-46

Authors: Lebedev MA, Nicolelis MA

Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm.

Oct 26, 2006

Brain Waves Drawing

Via Networked Performance

Brain Waves Drawing: Live Performance by Hideki Nakazawa: Nov 4-5, 2006 at Fuchu Art Museum, Tokyo Supported by Nihon Kohden.


Not to Draw by Hand. To Draw by Brain: Artists usually draw pictures by hand with brushes or pencils. However, the activities of brains must be more important and essential than the ones of hands at the moment of creating art. Therefore, I decided to draw pictures with electrodes being set on my head through controlling the activities of my own brain. The curved lines so-called "brain waves" in medicine must be the "drawings" in the world of fine art, directly drawn by my brain without using hands.

Oct 11, 2006

EEG-based brain-computer interface

Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills.

Med Biol Eng Comput. 2006 Oct 7;

Authors: Mahmoudi B, Erfanian A

Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.

Sep 30, 2006

Brain-computer interfaces for control of neuroprostheses

Brain-computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation.

Biomed Tech (Berl). 2006;51(2):57-63

Authors: Müller-Putz GR, Scherer R, Pfurtscheller G, Rupp R

Transferring a brain-computer interface (BCI) from the laboratory environment into real world applications is directly related to the problem of identifying user intentions from brain signals without any additional information in real time. From the perspective of signal processing, the BCI has to have an uncued or asynchronous design. Based on the results of two clinical applications, where 'thought' control of neuroprostheses based on movement imagery in tetraplegic patients with a high spinal cord injury has been established, the general steps from a synchronous or cue-guided BCI to an internally driven asynchronous brain-switch are discussed. The future potential of BCI methods for various control purposes, especially for functional rehabilitation of tetraplegics using neuroprosthetics, is outlined.

Jul 29, 2006

Brain-activity interpretation competition won by Italian researchers

Via Mind Hacks 



A team of three Italian researchers (Emanuele Olivetti, Diego Sona, and Sriharsha Veeramachaneni) won $10000 in a brain-activity interpretation competition. Entrants were provided with the fMRI data and behavioural reports recorded when four people watched two movies. The competitors' task was to create an algorithm that could use the viewers ongoing brain activity to predict what they were thinking and feeling as the film unfolded.

The Italian team resulted to be the most accurate, with a correlation of .86 for basic features, such as whether an instant of the film contained music. The full results are here.

Jul 28, 2006

Brain-computer interfaces for 1-D and 2-D cursor control

Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials.

IEEE Trans Neural Syst Rehabil Eng
. 2006 Jun;14(2):225-9

Authors: Trejo LJ, Rosipal R, Matthews B

We have developed and tested two electroencephalogram (EEG)-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KPLS classifier to map power spectra of 62-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display. Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject's average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions. Target Practice also implements online control of two artifact sources: 1) removal of ocular artifact by linear subtraction of wavelet-smoothed vertical and horizontal electrooculograms (EOG) signals, 2) control of muscle artifact by inhibition of BCI training during periods of relatively high power in the 40-64 Hz band. The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from 12 electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point. As for Target Practice, Think Pointer also implements wavelet-based online removal of ocular artifact; however, in Think Pointer muscle artifact is controlled via adaptive normalization of the SSVEP. Training of the classifier requires about 3 min. We have tested our system in real-time operation in three human subjects. Across subjects and sessions, control accuracy ranged from 80% to 100% correct with lags of 1-5 s for movement initiation and turning. We have also developed a realistic demonstration of our system for control of a moving map display (http://ti.arc.nasa.gov/).

Jul 27, 2006

A P300 event-related potential brain-computer interface

A P300 event-related potential brain-computer interface (BCI): The effects of matrix size and inter stimulus interval on performance.

Biol Psychol. 2006 Jul 21;

Authors: Sellers EW, Krusienski DJ, McFarland DJ, Vaughan TM, Wolpaw JR

We describe a study designed to assess properties of a P300 brain-computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3x3 or 6x6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350ms). Online accuracy was highest for the 3x3 matrix 175-ms ISI condition, while bit rate was highest for the 6x6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88%. P300 amplitude was significantly greater for the attended stimulus and for the 6x6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication.

Jul 25, 2006

The Berlin brain-computer interface: EEG-based communication without subject training

Blankertz, B. Dornhege, G. Krauledat, M. Muller, K.-R. Kunzmann, V. Losch, F. Curio, G. 
Neural Systems and Rehabilitation Engineering, IEEE Transactions, June 2006, Volume: 14,  Issue: 2
The Berlin Brain-Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are 1) the use of well-established motor competences as control paradigms, 2) high-dimensional features from 128-channel electroencephalogram (EEG), and 3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left- versus right-hand movements in healthy subjects. A more recent study showed that the RP similarly accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach, oscillatory features are used to discriminate imagined movements (left hand versus right hand versus foot). In a recent feedback study with six healthy subjects with no or very little experience with BCI control, three subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems.

Jul 24, 2006

Surfing the Web with nothing but brainwaves

Re-blogged from Smart Mobs 

Someday, keyboards and computer mice will be remembered only as medieval-style torture devices for the wrists. All work - emails, spreadsheets, and Google searches - will be performed by mind control. CNN reports via digg.

If you think that's mind-blowing, try to wrap your head around the sensational research that's been done on the brain of one Matthew Nagle by scientists at Brown University and three other institutions, in collaboration with Foxborough, Mass.-based company Cyberkinetics Neurotechnology Systems. The research was published for the first time last week in the British science journal Nature.

--- Controlling devices with the mind is just the beginning. Next, Wolf believes, is what he calls "network-enabled telepathy" - instant thought transfer. In other words, your thoughts will flow from your brain over the network right into someone else's brain. If you think instant messaging is addictive, just wait for instant thinking.

Jul 20, 2006

Using thought power to control artificial limbs

Neuronal ensemble control of prosthetic devices by a human with tetraplegia

Nature 442, 164-171(13 July 2006)

Leigh R. Hochberg, Mijail D. Serruya, Gerhard M. Friehs, Jon A. Mukand, Maryam Saleh, Abraham H. Caplan, Almut Branner, David Chen, Richard D. Penn and John P. Donoghue

Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a 'neural cursor' with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis.


Jul 18, 2006

A high-performance brain-computer interface

A high-performance brain-computer interface.

Nature. 2006 Jul 13;442(7099):195-8

Authors: Santhanam G, Ryu SI, Yu BM, Afshar A, Shenoy KV

Recent studies have demonstrated that monkeys and humans can use signals from the brain to guide computer cursors. Brain-computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques or electroencephalogram recordings using less- or non-invasive techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or approximately 15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.

Second Geoethical Nanotechnology workshop

Re-blogged from KurzweilAI.net

The Terasem Movement announced today that its Second Geoethical Nanotechnology workshop will be held July 20, 2006 in Lincoln, Vermont. The public is invited to participate via conference call.

The workshop will explore the ethics of neuronanotechnology and future mind-machine interfaces, including preservation of consciousness, implications for a future in which human and digital species merge, and dispersion of consciousness to the cosmos, featuring leading scientists and other experts in these areas.

The workshop proceedings are open to the public via real-time conference call and will be archived online for free public access. The public is invited to call a toll-free conference-call dial-in line from 9:00 a.m. - 6:00 p.m. ET. Callers from the continental US and Canada can dial 1-800-967-7135; other countries: (00+1) 719-457-2626.

Each workshop presentation is designed for a 15-20 minute delivery, followed by a 20 minute formal question and answer period, during which time questions from the worldwide audience will be invited. Presentations will also be available on the workshop's website 

Novel BCI device will allow people to search through images faster

Via KurzweilAI.net 

Researchers at Columbia University are combining the processing power of the human brain with computer vision to develop a novel device that will allow people to search through images ten times faster than they can on their own. 

The "cortically coupled computer vision system," known as C3 Vision, is the brainchild of professor Paul Sajda, director of the Laboratory for Intelligent Imaging and Neural Computing at Columbia University. He received a one-year, $758,000 grant from Darpa for the project in late 2005.

The brain emits a signal as soon as it sees something interesting, and that "aha" signal can be detected by an electroencephalogram, or EEG cap. While users sift through streaming images or video footage, the technology tags the images that elicit a signal, and ranks them in order of the strength of the neural signatures. Afterwards, the user can examine only the information that their brains identified as important, instead of wading through thousands of images.

Read the full story on Wired 

Jul 17, 2006


In a study published in the journal Nature this week, researchers from Boston-based Cyberkinetics Neurotechnology Systems describe how two paralyzed patients with a surgically implanted neural device successfully controlled a computer and, in one case, a robotic arm, using only their thoughts. 

These findings include the ability to voluntarily generate signals in the dorsal pre-motor cortex, the area of the brain responsible for the planning, selection and execution of movement. While accuracy levels have been previously published, the current study reveals unprecedented speed in retrieving and interpreting the neural signals that can be applied to the operation of external devices that require fast, accurate selections, such as typing.

The brain-computer interface used in the study consists of an internal sensor to detect brain cell activity and external processors that convert these brain signals into a computer-mediated output under the person's own control.



According to John Donoghue, Chief Scientific Officer of Cyberkinetics, and a co-inventor of the BrainGate technology, "The results achieved from this study demonstrate the utility and versatility of Cyberkinetics' neural sensing technology to achieve very rapid, accurate decoding - about as fast as humans ordinarily make decisions to move when asked. The contributions of complementary research with our electrode and data acquisition technology should enhance our development of the BrainGate System in its ability to, one day, enable those with severe paralysis or other neurological conditions to lead more independent lives."

See video here 

Jul 06, 2006

Third International Meeting on Brain-Computer Interface Technology

This special issue of the IEEE Transactions on Neural Systems and Rehabilitation Engineering provides a representative and comprehensive bird's-eye view of the most recent developments in brain–computer interface (BCI) technology from laboratories around the world. The 30 research communications and papers are the direct outcome of the Third International Meeting on Brain–Computer Interface Technology held at the Rensselaerville Institute, Rensselaerville, NY, in June 2005. Fifty-three research groups from North and South America, Europe, and Asia, representing the majority of all the existing BCI laboratories around the world, participated in this highly focused meeting sponsored by the National Institutes of Health and organized by the BCI Laboratory of the Wadsworth Center of the New York State Department of Health. As demonstrated by the papers in this special issue, the rapid advances in BCI research and development make this technology capable of providing communication and control to people severely disabled by amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, and other neuromuscular disorders. Future work is expected to improve the performance and utility of BCIs, and to focus increasingly on making them a viable, practical, and affordable communication alternative for many thousands of severely disabled people worldwide.

Jul 03, 2006

Brain waves allows disabled to take a virtual stroll

From The Observer

A new 'virtual helmet' which harnesses the power of brain waves is allowing severely disabled people to feel as if they can walk and move again, opening up the prospect of using the mind to help them control wheelchairs, computers and even false limbs.

Just by imagining their feet moving, patients using wheelchairs can again experience what it feels like to stroll down a high street, thanks to the work of British scientists who have found a new way of using the power of thought. They have devised the helmet which can link brain wave patterns to a virtual reality system, allowing the wearer to enter an illusory world of movement.

The new system has been tried out for the first time by an Austrian man who became a paraplegic after a swimming accident. Tom Schweiger was injured on holiday in Greece seven years ago when a huge wave swept him on to rocks, severing the spinal cord in his neck and leaving him paralysed apart from some movement in his left arm.

Last week 31-year-old Schweiger was able to enter a different virtual world when the scientists from his home country and a team at University College London tested the new system. When he was asked by researchers to think about moving either his foot or his hand, the changes in his brain waves - or electroencephalogram (EEG) signals - were recorded by electrodes on the top of his head. These were then turned into a control signal which was linked up to the virtual reality system.

Schweiger was given special 3D glasses to wear so that the images created in the 'virtual cave' created for the experiment, made up of a four-sided room complete with stereo sound and projected images, gave him the illusion of walking through a street. Different characters appeared on the screen and talked to him and he was asked to respond...


Read the full article 

May 30, 2006

Decoding the visual and subjective contents of the human brain

Decoding the visual and subjective contents of the human brain 

Yukiyasu Kamitani & Frank Tong

Nature Neuroscience  8, 679 - 685 (2005) 

The potential for human neuroimaging to read out the detailed contents of a person's mental state has yet to be fully explored. We investigated whether the perception of edge orientation, a fundamental visual feature, can be decoded from human brain activity measured with functional magnetic resonance imaging (fMRI). Using statistical algorithms to classify brain states, we found that ensemble fMRI signals in early visual areas could reliably predict on individual trials which of eight stimulus orientations the subject was seeing. Moreover, when subjects had to attend to one of two overlapping orthogonal gratings, feature-based attention strongly biased ensemble activity toward the attended orientation. These results demonstrate that fMRI activity patterns in early visual areas, including primary visual cortex (V1), contain detailed orientation information that can reliably predict subjective perception. Our approach provides a framework for the readout of fine-tuned representations in the human brain and their subjective contents.

May 23, 2006

Limits of brain-computer interface

Limits of brain-computer interface. Case report.

Neurosurg Focus. 2006;20(5):e6

Authors: Bakay RA

Most patients who are candidates for brain-computer interface studies have an injury to their central nervous system and therefore may not be ideal for rigorous testing of the full abilities and limits of the interface. This is a report on a quadriplegic patient who appeared to be a reasonable candidate for intracranial implantation of neurotrophic electrodes. He had significant cortical atrophy in both the motor and parietal cortical areas but was able to generate signal changes on functional magnetic resonance images by thinking about hand movements. Only a few low-amplitude action potentials were obtained, however, and he was unable to achieve single-unit control. Despite this failure, the use of field potentials offered an alternative method of control and allowed him some limited computer interactions. There are clearly limits to what can be achieved with brain-computer interfaces, and the presence of cortical atrophy should serve as a warning for future investigators that less invasive techniques may be a more prudent approach for this type of patient.

May 21, 2006

Towards adaptive classification for BCI

Towards adaptive classification for BCI.

J Neural Eng. 2006 Mar;3(1):R13-23

Authors: Shenoy P, Krauledat M, Blankertz B, Rao RP, Müller KR

Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.

Towards adaptive classification for BCI

Towards adaptive classification for BCI.

J Neural Eng. 2006 Mar;3(1):R13-23

Authors: Shenoy P, Krauledat M, Blankertz B, Rao RP, Müller KR

Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.